Data Scenario and Model Hypothesis

Standard fit report for fits of SISCA to SOGHerring data.

Data Scenario: SOG_agg

Model Hypothesis: Mb0.532_h0.70

Species:

Stocks:

Final phase convergence diagnostics

Max Gradient: 4.5383079^{-4}

Objective Function value: -613.4518993

Time to fit model: 6.02

PD Hessian: TRUE

No. of Non-finite SEs: 0

MCMC convergence diagnostics

Number of chains: 4

Total number of iterations: 2000

Maximum scale reduction factor value: 1.0184646

Number of divergent transitions post-warmup: 0

Prop divergent transitions: 0

Number of low bulk ESS parameters: 0

Number of low tail ESS parameters: 0

Maximum used tree depth: 9

Model fits

At-a-glance

Time series of spawning biomass with scaled spawn indices (top),
recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for 
substocks of SOGHerring. Stocks are, from left to right,SOG.

Figure 1: Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of SOGHerring. Stocks are, from left to right,SOG.

Fits to data

Probability of detection of a spawning event,
with respect to spawning stock biomass. Lines show the modeled probability
and the points indicate whether a spawn was detected, with 0 
for no spawn detected, and 1 for a positive index.

Figure 2: Probability of detection of a spawning event, with respect to spawning stock biomass. Lines show the modeled probability and the points indicate whether a spawn was detected, with 0 for no spawn detected, and 1 for a positive index.

Average model fits to age data. Stocks are left to right, 
and gears are top to bottom.

Figure 3: Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Model fits to age data, averaged over stock and time. Gears are top to bottom.

Figure 4: Model fits to age data, averaged over stock and time. Gears are top to bottom.

Table 1: Estimated standard deviations for observational data. The first three columns show age data sampling error standard deviations from the logistic-normal compositional likelihood function, and the last column shows spawn survey index standard deviations on the log scale.
\(\tau^{age}_{Red}\) \(\tau^{age}_{SR}\) \(\tau^{age}_{Gn}\) \(\tau^{surv}_{Su}\) \(\tau^{surv}_{D}\) \(\sigma_{R,eff}\) \(\sigma_{M,eff}\)
SOG 0.662 0.309 0.534 0.358 0.277 0.516 0.421

Recruitment

Age-1 recruitments for all stocks. Equilibrium unfished recruitment $R_0$ is 
indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, 
with the average of estimated residuals shown by the horizontal red dashed line.

Figure 5: Age-1 recruitments for all stocks. Equilibrium unfished recruitment \(R_0\) is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Figure 6: Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Natural Mortality

Mt

Figure 7: Mt

densityDependent M

Figure 8: densityDependent M

Total natural mortality at age

Figure 9: Total natural mortality at age

Total mortality at age

Figure 10: Total mortality at age

Selectivity and Catch

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet.

Figure 11: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet.

Figure 12: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet.

Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Figure 13: Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Reference Points

Yield Curves

Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Figure 14: Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

1-year ahead catch tables

Stock specific fits

Optimisation performance

Objective function components

Table 2: Objective function components for data observations.
objFun obsSurface obsDive ageRed ageSR ageGill
Total -613.45 0 0 -44.5 -144.28 -94.18
SOG -613.45 0 0 -44.5 -144.28 -94.18
Table 3: Objective function components for standard (single level) and hyper-priors.
V1
objFun -613.450000
recDevs -232.820000
initDevs 13.060000
h 16.780000
M 1.560000
tvMdev 0.000000
IGtau_surf 0.390000
IGtau_dive 1.550000
tvSelAlpha 0.000000
tvSelBeta 0.000000
selAlphaRed 0.920000
selAlphaSR 0.920000
selAlphaGn 0.920000
selBetaRed 0.920000
selBetaSR 0.920000
selBetaGn 0.920000
lnB0 4.516021
lnRinit 7.616997
psiSOK 0.000000
Table 4: Objective function components for hierarchical (mult-level) priors.
V1
objFun -613.45
MDev 0.00
hDev 0.92
selAlphaDevR 0.92
selAlphaDevSR 0.92
selAlphaDevGn 0.92
selBetaDevR 0.92
selBetaDevSR 0.92
selBetaDevGn 0.92

Phase fit table

Table 5: Optimisation performance of SISCA for each phase.
phase objFun maxGrad nPar convCode convMsg time pdHess
1 541.27442 0.0001303 13 0 relative convergence (4) 0.331 FALSE
2 -19.63287 0.0000150 16 0 relative convergence (4) 0.238 FALSE
3 -43.32571 0.0004670 17 0 relative convergence (4) 0.219 FALSE
4 -75.42739 0.0012045 20 0 relative convergence (4) 0.258 FALSE
5 -306.24046 0.0024791 92 0 relative convergence (4) 0.327 FALSE
6 -473.11722 0.0032846 162 0 relative convergence (4) 1.307 FALSE
7 -615.31686 0.0032105 165 0 relative convergence (4) 1.567 FALSE
8 -613.45190 0.0004538 164 0 relative convergence (4) 1.769 FALSE
HMC NA NA NA NA NA 1363.235 NA

Leading Parameter SDReport

Table 6: SD report showing leading parameter estimates, standard errors, gradient components, and coefficients of variation. Gradients with a magnitude above 1e-3 are shown in bold red, while the coefficients of variation (cv) are
coloured so that smaller values are lighter in colour, and larger values are darker, with cvs above .5 in bold, and cvs above 3 in red.
est se gr cv
lnB0_p 4.5160 0.2266 0.066 0.0502
lnRbar_p 8.0475 0.1370 -0.12 0.017
lnm1 0.9591 0.2578 -0.11 0.2688
fDevs_ap 0.7260 0.2503 -5e-05 0.3449
fDevs_ap.1 0.5554 0.2087 -0.00011 0.3758
fDevs_ap.2 0.5921 0.2249 5.6e-05 0.3798
fDevs_ap.3 0.0281 0.2785 -4.1e-05 9.9224
fDevs_ap.4 -1.7190 0.6567 -5.2e-05 0.382
fDevs_ap.5 -1.3209 0.7149 -4.2e-05 0.5412
fDevs_ap.6 -0.9666 0.7715 -2.8e-05 0.7982
fDevs_ap.7 -0.6819 0.8231 -1.3e-05 1.2071
fDevs_ap.8 -0.4447 0.8719 3.2e-06 1.9604
fDevs_ap.9 -0.5131 0.8689 -1.8e-05 1.6934
lnSelAlpha_g 1.0042 0.0091 0.0037 0.0091
lnSelAlpha_g.1 1.0325 0.0077 0.012 0.0075
lnSelAlpha_g.2 1.5198 0.0075 -0.11 0.0049
lntauObsComb_pg -1.0279 0.1529 6.8e-05 0.1487
lntauObsComb_pg.1 -1.2854 0.1637 -2.6e-05 0.1274
recDevs_pt 0.1234 0.0471 0.00029 0.3814
recDevs_pt.1 0.0433 0.0469 0.00022 1.084
recDevs_pt.2 -0.0998 0.0467 2e-04 0.4676
recDevs_pt.3 -0.1529 0.0486 0.00021 0.3176
recDevs_pt.4 -0.0732 0.0485 0.00029 0.6625
recDevs_pt.5 0.0588 0.0440 0.00058 0.748
recDevs_pt.6 0.0139 0.0450 0.00053 3.239
recDevs_pt.7 -0.1152 0.0544 0.00034 0.4725
recDevs_pt.8 0.0853 0.0520 0.0011 0.6092
recDevs_pt.9 0.0401 0.0509 0.0011 1.2708
recDevs_pt.10 0.0384 0.0486 0.0014 1.2641
recDevs_pt.11 -0.1098 0.0551 0.001 0.502
recDevs_pt.12 -0.1559 0.0688 0.00075 0.4414
recDevs_pt.13 -0.0788 0.0764 0.0019 0.9693
recDevs_pt.14 -0.2029 0.0996 0.0035 0.4909
recDevs_pt.15 -0.2699 0.1003 0.0042 0.3717
recDevs_pt.16 -0.1285 0.0586 0.0018 0.4559
recDevs_pt.17 -0.1139 0.0537 0.0045 0.4718
recDevs_pt.18 -0.1833 0.0551 -0.0051 0.3007
recDevs_pt.19 -0.1993 0.0541 0.0055 0.2712
recDevs_pt.20 -0.1470 0.0522 -0.006 0.3547
recDevs_pt.21 -0.0764 0.0480 -0.011 0.6279
recDevs_pt.22 -0.1644 0.0481 0.003 0.2927
recDevs_pt.23 -0.0286 0.0469 -0.0095 1.637
recDevs_pt.24 -0.0510 0.0468 -0.0087 0.9188
recDevs_pt.25 -0.2078 0.0486 -0.0074 0.234
recDevs_pt.26 -0.0841 0.0485 -0.015 0.5763
recDevs_pt.27 -0.1410 0.0474 -0.013 0.3364
recDevs_pt.28 -0.1551 0.0488 -0.019 0.3145
recDevs_pt.29 -0.1825 0.0509 -0.017 0.2789
recDevs_pt.30 -0.2327 0.0546 -0.0056 0.2346
recDevs_pt.31 -0.1653 0.0525 -0.0098 0.3177
recDevs_pt.32 -0.0686 0.0469 -0.0063 0.6838
recDevs_pt.33 -0.2388 0.0472 -0.0042 0.1978
recDevs_pt.34 -0.0341 0.0451 -0.0037 1.3241
recDevs_pt.35 -0.3193 0.0476 -0.0024 0.1491
recDevs_pt.36 -0.0877 0.0448 -0.013 0.5109
recDevs_pt.37 -0.2132 0.0461 -0.0041 0.2164
recDevs_pt.38 -0.0159 0.0451 -0.026 2.8365
recDevs_pt.39 -0.0993 0.0455 -0.016 0.4587
recDevs_pt.40 -0.0447 0.0452 -0.025 1.0109
recDevs_pt.41 -0.2200 0.0476 -0.0075 0.2164
recDevs_pt.42 -0.0607 0.0470 -0.025 0.7737
recDevs_pt.43 0.0101 0.0465 -0.028 4.6132
recDevs_pt.44 0.0086 0.0454 -0.03 5.2661
recDevs_pt.45 -0.1859 0.0457 -0.014 0.2457
recDevs_pt.46 -0.0808 0.0457 -0.02 0.5648
recDevs_pt.47 -0.0340 0.0456 -0.022 1.3408
recDevs_pt.48 0.0250 0.0457 -0.023 1.8265
recDevs_pt.49 0.0500 0.0458 -0.016 0.9166
recDevs_pt.50 -0.0433 0.0472 -0.01 1.0889
recDevs_pt.51 -0.0700 0.0490 -0.018 0.7001
recDevs_pt.52 -0.1072 0.0504 -0.02 0.47
recDevs_pt.53 0.0356 0.0483 -0.021 1.3574
recDevs_pt.54 -0.3076 0.0519 -0.0061 0.1688
recDevs_pt.55 0.0201 0.0465 -0.019 2.3205
recDevs_pt.56 -0.3836 0.0493 -0.0093 0.1284
recDevs_pt.57 0.0743 0.0449 -0.012 0.604
recDevs_pt.58 -0.0361 0.0444 -0.01 1.232
recDevs_pt.59 -0.1525 0.0445 0.0035 0.2921
recDevs_pt.60 0.0038 0.0429 -0.0039 11.2159
recDevs_pt.61 0.0335 0.0432 0.012 1.2889
recDevs_pt.62 -0.0046 0.0443 0.01 9.5786
recDevs_pt.63 -0.0334 0.0454 0.0014 1.3589
recDevs_pt.64 -0.0304 0.0471 -0.0061 1.5481
recDevs_pt.65 -0.0288 0.0477 -0.004 1.6583
recDevs_pt.66 0.1257 0.0491 -0.0047 0.3904
recDevs_pt.67 0.0472 0.0492 0.00083 1.0414
recDevs_pt.68 -0.0757 0.0535 0.002 0.706
recDevs_pt.69 0.0054 0.0540 0.0015 9.9765
omegaM_pt -0.2674 0.9296 0.00012 3.4771
omegaM_pt.1 -0.4581 0.8860 6.2e-05 1.9341
omegaM_pt.2 -0.5186 0.8746 2.6e-05 1.6865
omegaM_pt.3 -0.6953 0.8608 4.8e-08 1.2381
omegaM_pt.4 -0.6410 0.8595 -9e-06 1.3409
omegaM_pt.5 -0.6170 0.8680 -7e-06 1.4069
omegaM_pt.6 -0.6437 0.8569 -1.5e-05 1.3312
omegaM_pt.7 -1.0925 0.8438 -3.3e-05 0.7724
omegaM_pt.8 -0.3078 0.8705 -3.9e-05 2.8282
omegaM_pt.9 0.1702 0.8982 -5.7e-05 5.2764
omegaM_pt.10 -0.0839 0.9015 -6.8e-05 10.7474
omegaM_pt.11 -0.5781 0.8750 -7.7e-05 1.5137
omegaM_pt.12 -0.4544 0.8839 -7.2e-05 1.9452
omegaM_pt.13 0.3112 0.9144 -5.9e-05 2.9383
omegaM_pt.14 0.7351 0.9433 -0.00011 1.2832
omegaM_pt.15 -0.0685 0.9221 -0.00022 13.4525
omegaM_pt.16 0.8240 0.8921 -0.00046 1.0826
omegaM_pt.17 0.1206 0.9031 -0.00032 7.4909
omegaM_pt.18 -0.0792 0.8957 -0.00038 11.3067
omegaM_pt.19 0.1340 0.8945 -6.2e-05 6.6729
omegaM_pt.20 0.3002 0.8959 -0.00027 2.9846
omegaM_pt.21 -0.1676 0.8883 8.4e-05 5.2991
omegaM_pt.22 -0.8191 0.8696 0.00042 1.0617
omegaM_pt.23 -0.6104 0.8564 0.00027 1.403
omegaM_pt.24 -0.4388 0.8584 0.00054 1.9565
omegaM_pt.25 -0.5739 0.8516 0.00069 1.4838
omegaM_pt.26 -0.4439 0.8584 0.00082 1.9339
omegaM_pt.27 -0.3861 0.8710 0.0011 2.256
omegaM_pt.28 -0.5110 0.8640 0.0013 1.6909
omegaM_pt.29 -0.3053 0.8692 0.0018 2.8474
omegaM_pt.30 -0.2166 0.8758 0.0021 4.0432
omegaM_pt.31 0.6949 0.8890 0.0022 1.2793
omegaM_pt.32 0.6680 0.9144 0.0018 1.369
omegaM_pt.33 -0.0007 0.8753 0.00095 1269.6325
omegaM_pt.34 -0.2245 0.8362 7e-04 3.7248
omegaM_pt.35 -0.5531 0.8456 0.00057 1.5288
omegaM_pt.36 -0.1554 0.8169 0.00053 5.2563
omegaM_pt.37 -1.0029 0.8361 7e-04 0.8337
omegaM_pt.38 -0.4315 0.8248 0.00079 1.9114
omegaM_pt.39 -0.2179 0.8287 0.0015 3.8033
omegaM_pt.40 -0.4316 0.8297 0.0018 1.9224
omegaM_pt.41 -0.4820 0.8305 0.0022 1.7231
omegaM_pt.42 0.0491 0.8289 0.0024 16.8728
omegaM_pt.43 0.2383 0.8404 0.0027 3.5271
omegaM_pt.44 0.1462 0.8514 0.003 5.8223
omegaM_pt.45 -0.0302 0.8464 0.0033 28.063
omegaM_pt.46 -0.2883 0.8433 0.0028 2.9248
omegaM_pt.47 -0.3168 0.8366 0.0027 2.6409
omegaM_pt.48 -0.2352 0.8416 0.0027 3.5788
omegaM_pt.49 -0.3199 0.8405 0.0026 2.6273
omegaM_pt.50 -0.4010 0.8355 0.0025 2.0834
omegaM_pt.51 -0.2186 0.8393 0.0023 3.8389
omegaM_pt.52 0.0387 0.8559 0.0026 22.135
omegaM_pt.53 0.3465 0.8764 0.0029 2.5295
omegaM_pt.54 0.2257 0.9013 0.0027 3.9924
omegaM_pt.55 0.3382 0.8517 0.0022 2.5179
omegaM_pt.56 -0.6846 0.8466 0.0018 1.2366
omegaM_pt.57 -0.2877 0.8432 0.0017 2.9311
omegaM_pt.58 -0.3296 0.8501 0.0014 2.5794
omegaM_pt.59 -0.3856 0.8518 0.0013 2.2092
omegaM_pt.60 -0.4358 0.8479 0.00086 1.9457
omegaM_pt.61 -0.7297 0.8287 0.00057 1.1357
omegaM_pt.62 -0.5714 0.8254 -4.6e-05 1.4445
omegaM_pt.63 -0.5283 0.8337 -0.00046 1.5782
omegaM_pt.64 -0.4090 0.8380 -0.00046 2.0492
omegaM_pt.65 0.0779 0.8497 -0.00017 10.9123
omegaM_pt.66 0.3067 0.8519 0.00014 2.7775
omegaM_pt.67 0.4375 0.8608 0.00043 1.9674
omegaM_pt.68 0.2465 0.8647 0.00032 3.5078
omegaM_pt.69 0.7348 0.8699 0.00019 1.1838
omegaM_pt.70 0.1601 0.8845 2.5e-05 5.5234
omegaM_pt.71 0.1121 0.9205 1.2e-05 8.2116
lnqComb_pg -0.2981 0.0938 8.4e-05 0.3146
logitphi1_g 2.1307 0.3782 -2.2e-05 0.1775
logitphi1_g.1 1.0553 0.3202 -2.6e-06 0.3034
logitphi1_g.2 1.9176 0.3322 -0.011 0.1733

MCMC posteriors

MCMC performance

Visual diagnostics

MCMC diagnostics plots. The bulk of Rhat should be below 1.01. Lag-1 Autocorrelation should be unimodal centered around 0 or slightly below 0. Bulk effective sample size, and tai effective sample size should be higher than a threshold. CV(theta) small is good.

Figure 15: MCMC diagnostics plots. The bulk of Rhat should be below 1.01. Lag-1 Autocorrelation should be unimodal centered around 0 or slightly below 0. Bulk effective sample size, and tai effective sample size should be higher than a threshold. CV(theta) small is good.

Hamiltonian Monte Carlo local Estimated Sample Size for lnRbar_p.Figures should look random with no trends.

Figure 16: Hamiltonian Monte Carlo local Estimated Sample Size for lnRbar_p.Figures should look random with no trends.

HMC quantile ESS forlnRbar_p.

Figure 17: HMC quantile ESS forlnRbar_p.

Change in ESS as a function of chain length forlnRbar_p.

Figure 18: Change in ESS as a function of chain length forlnRbar_p.

Hamiltonian Monte Carlo local Estimated Sample Size for lnRbar_p.Figures should look random with no trends.

Figure 19: Hamiltonian Monte Carlo local Estimated Sample Size for lnRbar_p.Figures should look random with no trends.

Biological parameters pairs plot

Marginal and joint posteriors for selected parameters.Red dots indicate divergent transitions. Yellow dots indicate max tree depths was reached.

Figure 20: Marginal and joint posteriors for selected parameters.Red dots indicate divergent transitions. Yellow dots indicate max tree depths was reached.

Monitor table

Table 7: HMC marginal posterior summary table
Q5 Q50 Q95 Mean SD MCSE_Q5 MCSE_Q50 MCSE_Q95 MCSE_Mean MCSE_SD Rhat Bulk_ESS Tail_ESS
lnB0_p 4.310 4.676 4.933 4.659 0.188 0.017 0.006 0.010 0.006 0.004 1.004 1020 1154
lnRbar_p 7.800 7.997 8.228 8.004 0.131 0.008 0.006 0.013 0.006 0.004 1.005 502 659
lnm1 0.794 1.326 1.828 1.318 0.313 0.019 0.012 0.012 0.011 0.008 1.005 848 947
fDevs_ap[1] 0.356 0.766 1.241 0.773 0.271 0.011 0.009 0.019 0.008 0.006 1.003 1139 1286
fDevs_ap[2] 0.191 0.564 0.965 0.568 0.234 0.016 0.008 0.014 0.007 0.005 1.002 1043 1254
fDevs_ap[3] 0.152 0.533 0.927 0.533 0.231 0.012 0.008 0.014 0.007 0.005 1.004 1036 1423
fDevs_ap[4] -0.484 -0.071 0.400 -0.062 0.269 0.010 0.008 0.025 0.008 0.006 1.005 1168 1360
fDevs_ap[5] -3.036 -1.895 -0.930 -1.927 0.649 0.040 0.019 0.023 0.014 0.011 1.002 2081 1382
fDevs_ap[6] -2.721 -1.518 -0.445 -1.549 0.695 0.058 0.018 0.043 0.014 0.010 1.000 2577 1746
fDevs_ap[7] -2.467 -1.174 -0.086 -1.190 0.727 0.062 0.019 0.026 0.014 0.012 1.001 2738 1606
fDevs_ap[8] -2.204 -0.882 0.268 -0.905 0.758 0.045 0.020 0.033 0.014 0.013 1.002 2824 1806
fDevs_ap[9] -1.996 -0.606 0.599 -0.647 0.796 0.044 0.016 0.042 0.013 0.014 1.003 3848 1586
fDevs_ap[10] -2.208 -0.750 0.520 -0.774 0.810 0.059 0.023 0.024 0.016 0.015 1.000 2554 1455
lnSelAlpha_g[1] 0.986 1.002 1.017 1.002 0.009 0.001 0.000 0.001 0.000 0.000 1.003 2459 1625
lnSelAlpha_g[2] 1.017 1.031 1.044 1.030 0.008 0.000 0.000 0.000 0.000 0.000 1.005 2797 1235
lnSelAlpha_g[3] 1.506 1.518 1.530 1.518 0.008 0.000 0.000 0.000 0.000 0.000 1.003 3130 1675
lntauObsComb_pg[1] -1.150 -0.899 -0.631 -0.897 0.155 0.007 0.004 0.012 0.003 0.002 1.001 2589 1641
lntauObsComb_pg[2] -1.420 -1.162 -0.890 -1.158 0.163 0.007 0.005 0.011 0.004 0.003 1.001 1603 1626
recDevs_pt[1] 0.052 0.129 0.217 0.130 0.050 0.002 0.002 0.004 0.002 0.001 1.005 759 944
recDevs_pt[2] -0.029 0.050 0.140 0.052 0.052 0.003 0.002 0.004 0.002 0.001 1.004 839 1301
recDevs_pt[3] -0.178 -0.094 -0.012 -0.094 0.050 0.003 0.002 0.003 0.002 0.001 1.002 865 1066
recDevs_pt[4] -0.235 -0.153 -0.071 -0.153 0.050 0.003 0.002 0.003 0.001 0.001 1.001 1188 1501
recDevs_pt[5] -0.150 -0.070 0.012 -0.070 0.050 0.003 0.002 0.004 0.001 0.001 1.002 1116 1589
recDevs_pt[6] -0.012 0.059 0.137 0.061 0.045 0.002 0.001 0.003 0.001 0.001 1.003 931 1392
recDevs_pt[7] -0.057 0.017 0.099 0.018 0.048 0.003 0.001 0.002 0.001 0.001 1.002 1216 1379
recDevs_pt[8] -0.208 -0.112 -0.027 -0.114 0.055 0.003 0.002 0.003 0.002 0.001 1.001 1174 1114
recDevs_pt[9] -0.001 0.085 0.172 0.085 0.053 0.003 0.002 0.003 0.002 0.001 1.002 984 1240
recDevs_pt[10] -0.038 0.042 0.128 0.043 0.051 0.003 0.002 0.003 0.002 0.001 1.002 1151 1330
recDevs_pt[11] -0.039 0.041 0.126 0.042 0.051 0.003 0.002 0.004 0.001 0.001 1.003 1305 1352
recDevs_pt[12] -0.207 -0.112 -0.011 -0.111 0.059 0.004 0.002 0.002 0.001 0.001 1.003 1723 1700
recDevs_pt[13] -0.285 -0.159 -0.042 -0.160 0.074 0.004 0.001 0.002 0.002 0.001 1.002 2219 1722
recDevs_pt[14] -0.245 -0.109 0.016 -0.111 0.080 0.004 0.002 0.004 0.002 0.001 1.001 2021 1456
recDevs_pt[15] -0.420 -0.234 -0.071 -0.238 0.105 0.009 0.003 0.006 0.002 0.002 1.001 2073 1444
recDevs_pt[16] -0.488 -0.309 -0.147 -0.312 0.105 0.006 0.003 0.008 0.002 0.002 1.000 2255 1479
recDevs_pt[17] -0.241 -0.140 -0.040 -0.142 0.061 0.004 0.002 0.003 0.002 0.001 1.005 1210 1675
recDevs_pt[18] -0.215 -0.122 -0.029 -0.122 0.057 0.004 0.002 0.003 0.002 0.001 1.005 1343 1388
recDevs_pt[19] -0.288 -0.190 -0.085 -0.189 0.060 0.003 0.002 0.003 0.002 0.001 1.002 1160 1373
recDevs_pt[20] -0.298 -0.199 -0.101 -0.200 0.061 0.004 0.002 0.005 0.002 0.001 1.004 995 1245
recDevs_pt[21] -0.237 -0.144 -0.053 -0.145 0.056 0.004 0.002 0.003 0.002 0.001 1.006 769 1049
recDevs_pt[22] -0.160 -0.071 0.015 -0.072 0.054 0.003 0.003 0.002 0.002 0.001 1.006 1044 1365
recDevs_pt[23] -0.244 -0.162 -0.074 -0.161 0.052 0.003 0.002 0.003 0.002 0.001 1.007 928 1408
recDevs_pt[24] -0.106 -0.027 0.059 -0.025 0.052 0.002 0.002 0.003 0.002 0.001 1.007 943 1471
recDevs_pt[25] -0.126 -0.050 0.036 -0.047 0.050 0.003 0.002 0.002 0.002 0.001 1.002 1032 1086
recDevs_pt[26] -0.288 -0.207 -0.121 -0.206 0.051 0.002 0.002 0.003 0.002 0.001 1.001 1077 1280
recDevs_pt[27] -0.170 -0.085 0.007 -0.083 0.053 0.004 0.002 0.003 0.002 0.001 1.003 1090 1059
recDevs_pt[28] -0.224 -0.142 -0.054 -0.142 0.051 0.003 0.002 0.004 0.002 0.001 1.006 847 1107
recDevs_pt[29] -0.241 -0.157 -0.066 -0.157 0.053 0.003 0.002 0.003 0.002 0.001 1.002 1006 1327
recDevs_pt[30] -0.272 -0.187 -0.091 -0.185 0.055 0.003 0.002 0.004 0.002 0.001 1.001 1173 1227
recDevs_pt[31] -0.338 -0.238 -0.142 -0.238 0.058 0.005 0.002 0.004 0.002 0.001 1.001 1039 1226
recDevs_pt[32] -0.255 -0.172 -0.076 -0.169 0.055 0.003 0.002 0.002 0.002 0.001 1.002 1094 1381
recDevs_pt[33] -0.150 -0.067 0.012 -0.069 0.050 0.003 0.001 0.003 0.002 0.001 1.001 1099 1237
recDevs_pt[34] -0.315 -0.237 -0.153 -0.236 0.050 0.003 0.002 0.003 0.001 0.001 1.001 1197 1535
recDevs_pt[35] -0.105 -0.030 0.053 -0.029 0.048 0.002 0.002 0.003 0.002 0.001 1.006 894 1312
recDevs_pt[36] -0.398 -0.316 -0.234 -0.315 0.050 0.003 0.002 0.003 0.001 0.001 1.002 1220 1321
recDevs_pt[37] -0.161 -0.084 -0.009 -0.084 0.046 0.004 0.001 0.003 0.001 0.001 1.004 1069 1092
recDevs_pt[38] -0.289 -0.210 -0.135 -0.211 0.048 0.003 0.002 0.002 0.002 0.001 1.006 941 1279
recDevs_pt[39] -0.087 -0.011 0.063 -0.012 0.046 0.003 0.002 0.003 0.002 0.001 1.006 852 1380
recDevs_pt[40] -0.169 -0.095 -0.016 -0.095 0.047 0.003 0.002 0.002 0.002 0.001 1.008 861 1344
recDevs_pt[41] -0.121 -0.042 0.039 -0.042 0.049 0.002 0.002 0.003 0.002 0.001 1.006 1054 1446
recDevs_pt[42] -0.298 -0.218 -0.136 -0.217 0.049 0.001 0.001 0.004 0.002 0.001 1.003 1061 1223
recDevs_pt[43] -0.139 -0.059 0.024 -0.059 0.048 0.003 0.001 0.003 0.002 0.001 1.001 1003 1338
recDevs_pt[44] -0.066 0.011 0.095 0.012 0.049 0.004 0.002 0.004 0.002 0.001 1.000 1048 1223
recDevs_pt[45] -0.065 0.009 0.093 0.010 0.047 0.003 0.002 0.004 0.002 0.001 1.002 951 1309
recDevs_pt[46] -0.262 -0.187 -0.106 -0.186 0.048 0.002 0.002 0.004 0.002 0.001 1.000 907 1106
recDevs_pt[47] -0.160 -0.080 0.002 -0.081 0.049 0.003 0.001 0.002 0.002 0.001 1.002 943 1233
recDevs_pt[48] -0.114 -0.035 0.049 -0.034 0.048 0.004 0.002 0.004 0.002 0.001 1.003 880 1345
recDevs_pt[49] -0.054 0.024 0.106 0.024 0.048 0.005 0.002 0.004 0.002 0.001 1.003 777 1079
recDevs_pt[50] -0.031 0.046 0.126 0.048 0.048 0.004 0.002 0.002 0.002 0.001 1.006 689 919
recDevs_pt[51] -0.129 -0.048 0.033 -0.048 0.049 0.003 0.002 0.003 0.002 0.001 1.005 715 1135
recDevs_pt[52] -0.159 -0.079 0.008 -0.078 0.051 0.002 0.003 0.003 0.002 0.001 1.008 682 1303
recDevs_pt[53] -0.200 -0.113 -0.029 -0.114 0.052 0.002 0.002 0.003 0.002 0.001 1.006 911 1182
recDevs_pt[54] -0.053 0.032 0.115 0.031 0.051 0.003 0.002 0.002 0.002 0.001 1.004 1055 1558
recDevs_pt[55] -0.402 -0.311 -0.217 -0.311 0.056 0.004 0.002 0.002 0.002 0.001 1.002 907 1077
recDevs_pt[56] -0.058 0.015 0.092 0.016 0.046 0.003 0.002 0.003 0.001 0.001 1.001 1006 1344
recDevs_pt[57] -0.471 -0.387 -0.302 -0.386 0.051 0.004 0.002 0.002 0.002 0.001 1.003 936 1330
recDevs_pt[58] -0.006 0.070 0.152 0.071 0.048 0.003 0.002 0.003 0.002 0.001 1.001 927 1263
recDevs_pt[59] -0.118 -0.040 0.042 -0.039 0.048 0.002 0.002 0.003 0.002 0.001 1.004 960 1258
recDevs_pt[60] -0.232 -0.155 -0.076 -0.154 0.047 0.003 0.001 0.004 0.001 0.001 1.002 996 1269
recDevs_pt[61] -0.072 0.000 0.076 0.002 0.046 0.003 0.002 0.003 0.002 0.001 1.003 867 1076
recDevs_pt[62] -0.046 0.030 0.110 0.030 0.047 0.003 0.002 0.003 0.002 0.001 1.003 879 1067
recDevs_pt[63] -0.086 -0.010 0.063 -0.010 0.046 0.003 0.002 0.003 0.002 0.001 1.000 860 1115
recDevs_pt[64] -0.117 -0.038 0.039 -0.038 0.047 0.003 0.001 0.003 0.002 0.001 1.001 956 1105
recDevs_pt[65] -0.112 -0.036 0.048 -0.035 0.050 0.003 0.002 0.003 0.002 0.001 1.002 904 1135
recDevs_pt[66] -0.114 -0.031 0.050 -0.031 0.050 0.003 0.002 0.003 0.002 0.001 1.001 1080 1204
recDevs_pt[67] 0.044 0.126 0.204 0.126 0.048 0.003 0.002 0.002 0.001 0.001 1.002 1176 1422
recDevs_pt[68] -0.032 0.046 0.130 0.047 0.050 0.003 0.002 0.002 0.001 0.001 1.000 1276 1394
recDevs_pt[69] -0.165 -0.075 0.011 -0.077 0.054 0.002 0.002 0.002 0.001 0.001 0.999 1604 1610
recDevs_pt[70] -0.086 0.004 0.098 0.005 0.057 0.003 0.002 0.004 0.001 0.001 1.001 1758 1478
omegaM_pt[1] -1.590 -0.068 1.515 -0.057 0.949 0.051 0.027 0.052 0.020 0.019 1.003 2248 1777
omegaM_pt[2] -1.855 -0.403 0.979 -0.424 0.855 0.034 0.019 0.040 0.017 0.014 1.003 2469 1706
omegaM_pt[3] -2.047 -0.508 0.972 -0.506 0.908 0.055 0.031 0.061 0.018 0.015 1.002 2404 1139
omegaM_pt[4] -2.188 -0.595 0.802 -0.631 0.924 0.055 0.026 0.032 0.018 0.018 1.003 2724 1355
omegaM_pt[5] -1.990 -0.551 0.819 -0.562 0.851 0.045 0.027 0.057 0.017 0.015 1.002 2586 1343
omegaM_pt[6] -1.896 -0.492 0.886 -0.485 0.843 0.048 0.019 0.065 0.015 0.015 1.001 3133 1714
omegaM_pt[7] -1.924 -0.551 0.790 -0.553 0.824 0.047 0.019 0.040 0.015 0.015 1.005 2973 1531
omegaM_pt[8] -2.392 -0.995 0.396 -0.994 0.840 0.050 0.021 0.048 0.016 0.013 1.000 2710 1868
omegaM_pt[9] -1.777 -0.290 1.016 -0.307 0.856 0.054 0.019 0.032 0.015 0.015 0.999 3234 1539
omegaM_pt[10] -1.342 0.133 1.555 0.117 0.897 0.049 0.018 0.050 0.019 0.019 1.000 2268 1549
omegaM_pt[11] -1.522 -0.076 1.243 -0.098 0.849 0.050 0.029 0.038 0.017 0.019 1.001 2557 1410
omegaM_pt[12] -2.045 -0.483 0.936 -0.508 0.906 0.062 0.025 0.037 0.017 0.021 1.005 2935 1534
omegaM_pt[13] -1.873 -0.357 1.090 -0.374 0.909 0.041 0.017 0.046 0.014 0.017 1.005 3946 1597
omegaM_pt[14] -1.288 0.339 1.799 0.294 0.939 0.081 0.022 0.047 0.017 0.021 1.001 2954 1358
omegaM_pt[15] -0.892 0.732 2.232 0.694 0.940 0.066 0.024 0.056 0.018 0.017 1.001 2628 1605
omegaM_pt[16] -1.567 0.088 1.558 0.053 0.958 0.060 0.021 0.038 0.017 0.022 1.000 3196 1644
omegaM_pt[17] -0.646 0.906 2.311 0.874 0.920 0.030 0.027 0.064 0.019 0.019 1.009 2439 1050
omegaM_pt[18] -1.305 0.282 1.771 0.254 0.933 0.038 0.019 0.042 0.019 0.019 1.001 2497 1771
omegaM_pt[19] -1.495 0.042 1.602 0.058 0.936 0.047 0.019 0.058 0.018 0.023 1.002 2612 1412
omegaM_pt[20] -1.229 0.294 1.792 0.285 0.914 0.054 0.019 0.035 0.017 0.019 0.999 2854 1536
omegaM_pt[21] -1.200 0.464 1.935 0.439 0.951 0.047 0.028 0.060 0.019 0.021 1.000 2603 1363
omegaM_pt[22] -1.567 0.043 1.505 0.008 0.939 0.048 0.027 0.048 0.021 0.023 1.001 2058 1490
omegaM_pt[23] -2.112 -0.617 0.801 -0.626 0.906 0.058 0.022 0.047 0.016 0.016 1.001 3225 1608
omegaM_pt[24] -1.917 -0.425 0.993 -0.446 0.893 0.047 0.021 0.039 0.016 0.019 1.003 3018 1681
omegaM_pt[25] -1.868 -0.320 1.006 -0.354 0.885 0.065 0.026 0.057 0.016 0.021 1.005 2837 1412
omegaM_pt[26] -2.051 -0.500 0.869 -0.526 0.872 0.055 0.031 0.047 0.017 0.018 1.003 2799 1578
omegaM_pt[27] -1.736 -0.326 0.970 -0.353 0.832 0.037 0.023 0.064 0.015 0.016 1.002 3226 1385
omegaM_pt[28] -1.865 -0.277 1.094 -0.320 0.898 0.038 0.023 0.031 0.016 0.016 1.001 3280 1674
omegaM_pt[29] -1.879 -0.399 0.918 -0.417 0.860 0.059 0.023 0.050 0.018 0.017 1.004 2185 1397
omegaM_pt[30] -1.722 -0.215 1.175 -0.241 0.882 0.067 0.022 0.047 0.015 0.018 1.001 3259 1396
omegaM_pt[31] -1.529 -0.051 1.292 -0.082 0.866 0.051 0.020 0.044 0.016 0.020 1.001 2855 1519
omegaM_pt[32] -0.776 0.817 2.200 0.786 0.890 0.060 0.025 0.059 0.019 0.018 1.002 2177 1444
omegaM_pt[33] -0.817 0.815 2.319 0.806 0.962 0.058 0.032 0.043 0.022 0.018 1.001 1902 1643
omegaM_pt[34] -1.325 0.156 1.649 0.148 0.921 0.048 0.021 0.045 0.019 0.029 1.002 2335 1414
omegaM_pt[35] -1.416 0.005 1.398 0.001 0.874 0.066 0.022 0.040 0.016 0.021 1.001 2974 1568
omegaM_pt[36] -1.791 -0.345 0.986 -0.369 0.853 0.049 0.018 0.034 0.017 0.017 1.001 2510 1581
omegaM_pt[37] -1.347 0.042 1.339 0.011 0.831 0.051 0.019 0.032 0.015 0.018 1.005 2964 1505
omegaM_pt[38] -2.198 -0.802 0.599 -0.794 0.840 0.063 0.021 0.032 0.016 0.014 1.000 2707 1539
omegaM_pt[39] -1.709 -0.254 1.062 -0.287 0.832 0.045 0.021 0.021 0.016 0.018 1.002 2852 1483
omegaM_pt[40] -1.537 -0.083 1.185 -0.124 0.817 0.045 0.023 0.040 0.016 0.017 1.008 2504 1743
omegaM_pt[41] -1.751 -0.314 0.876 -0.345 0.817 0.051 0.023 0.046 0.015 0.018 1.000 3001 1340
omegaM_pt[42] -1.845 -0.420 0.962 -0.430 0.847 0.031 0.028 0.033 0.017 0.018 1.006 2468 1427
omegaM_pt[43] -1.342 0.144 1.460 0.112 0.857 0.054 0.018 0.031 0.017 0.021 1.004 2458 1393
omegaM_pt[44] -1.153 0.349 1.631 0.295 0.852 0.050 0.030 0.036 0.017 0.019 1.000 2472 1425
omegaM_pt[45] -1.368 0.200 1.537 0.157 0.877 0.052 0.017 0.039 0.016 0.021 1.007 2918 1412
omegaM_pt[46] -1.436 0.100 1.378 0.047 0.861 0.043 0.019 0.038 0.016 0.019 1.005 2781 1566
omegaM_pt[47] -1.664 -0.162 1.124 -0.187 0.843 0.047 0.022 0.055 0.017 0.019 1.000 2525 1265
omegaM_pt[48] -1.624 -0.189 1.079 -0.227 0.827 0.053 0.019 0.064 0.015 0.018 1.000 2871 1293
omegaM_pt[49] -1.654 -0.157 1.136 -0.184 0.845 0.056 0.027 0.052 0.017 0.018 1.002 2575 1716
omegaM_pt[50] -1.814 -0.293 1.037 -0.323 0.874 0.045 0.024 0.039 0.016 0.018 1.003 3010 1528
omegaM_pt[51] -1.767 -0.362 0.930 -0.382 0.831 0.036 0.022 0.038 0.016 0.016 1.000 2509 1254
omegaM_pt[52] -1.700 -0.188 1.192 -0.215 0.868 0.062 0.024 0.065 0.018 0.018 1.000 2330 1463
omegaM_pt[53] -1.359 0.028 1.283 0.005 0.818 0.060 0.018 0.035 0.015 0.018 1.002 2976 1668
omegaM_pt[54] -1.224 0.330 1.711 0.303 0.895 0.034 0.025 0.040 0.018 0.020 1.001 2317 1314
omegaM_pt[55] -1.272 0.235 1.643 0.233 0.888 0.056 0.017 0.044 0.018 0.018 0.999 2619 1708
omegaM_pt[56] -1.062 0.478 1.869 0.465 0.887 0.033 0.025 0.039 0.018 0.019 1.001 2304 1600
omegaM_pt[57] -1.870 -0.455 0.889 -0.469 0.839 0.041 0.018 0.058 0.015 0.017 1.003 3240 1562
omegaM_pt[58] -1.580 -0.047 1.441 -0.057 0.918 0.035 0.024 0.049 0.018 0.023 1.001 2490 1534
omegaM_pt[59] -1.754 -0.175 1.233 -0.193 0.908 0.069 0.024 0.049 0.018 0.021 1.001 2588 1466
omegaM_pt[60] -1.787 -0.250 1.029 -0.304 0.851 0.052 0.021 0.063 0.017 0.017 1.000 2403 1422
omegaM_pt[61] -1.802 -0.302 1.046 -0.350 0.865 0.056 0.028 0.058 0.016 0.018 1.000 2918 1577
omegaM_pt[62] -2.019 -0.608 0.650 -0.627 0.810 0.056 0.030 0.048 0.015 0.014 1.000 2795 1440
omegaM_pt[63] -1.916 -0.540 0.772 -0.544 0.833 0.052 0.020 0.041 0.016 0.015 1.000 2683 1568
omegaM_pt[64] -1.915 -0.501 0.755 -0.546 0.834 0.050 0.019 0.064 0.017 0.014 1.001 2383 1643
omegaM_pt[65] -1.867 -0.403 0.886 -0.443 0.842 0.058 0.024 0.030 0.017 0.016 1.002 2559 1690
omegaM_pt[66] -1.445 0.080 1.384 0.020 0.873 0.047 0.026 0.042 0.017 0.020 1.001 2523 1422
omegaM_pt[67] -1.207 0.283 1.660 0.261 0.878 0.063 0.028 0.060 0.017 0.021 1.003 2689 1421
omegaM_pt[68] -1.087 0.393 1.716 0.356 0.858 0.044 0.019 0.038 0.017 0.017 1.006 2466 1626
omegaM_pt[69] -1.203 0.265 1.595 0.233 0.850 0.031 0.024 0.034 0.018 0.017 0.999 2114 1542
omegaM_pt[70] -0.890 0.655 2.002 0.626 0.867 0.064 0.019 0.039 0.017 0.015 1.000 2589 1705
omegaM_pt[71] -1.311 0.225 1.586 0.188 0.886 0.050 0.028 0.044 0.016 0.018 1.000 2918 1620
omegaM_pt[72] -1.587 0.084 1.610 0.061 0.966 0.059 0.018 0.052 0.017 0.023 1.003 3363 1344
lnqComb_pg -0.507 -0.328 -0.160 -0.331 0.106 0.007 0.003 0.006 0.003 0.002 1.001 1620 1472
logitphi1_g[1] 1.418 2.006 2.706 2.019 0.394 0.017 0.013 0.023 0.007 0.005 1.001 3563 949
logitphi1_g[2] 0.613 1.065 1.652 1.093 0.322 0.015 0.010 0.022 0.007 0.005 1.001 2530 1638
logitphi1_g[3] 1.409 1.903 2.505 1.927 0.335 0.014 0.007 0.016 0.006 0.004 1.001 3475 1400
lp__ 510.350 526.585 541.657 526.385 9.726 0.713 0.386 0.692 0.387 0.274 1.018 637 1187

Other

Compositional Likelihood Correlation Matrices

Estimated correlation matrices for age composition residuals in the  reduction  fleet. The circles above the visualise the numbers below the diagonal.

Figure 21: Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  seineRoe  fleet. The circles above the visualise the numbers below the diagonal.

Figure 22: Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the  gillnet  fleet. The circles above the visualise the numbers below the diagonal.

Figure 23: Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Comparisons with ISCAM

Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Figure 24: Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.

Figure 25: Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.